Beyond Consumption-Relevant Outcomes: The Role of AI Customer Service Chatbots’ Communication Styles in Promoting Societal Welfare

Yuanyuan Zhou , Jun Wang , Yixin Ding , Xinyu Meng , Ang Gao

Journal of Systems Science and Systems Engineering ›› 2025, Vol. 34 ›› Issue (4) : 448 -470.

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Journal of Systems Science and Systems Engineering ›› 2025, Vol. 34 ›› Issue (4) : 448 -470. DOI: 10.1007/s11518-025-5674-8
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Beyond Consumption-Relevant Outcomes: The Role of AI Customer Service Chatbots’ Communication Styles in Promoting Societal Welfare

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Abstract

The application of artificial intelligence (AI) in customer service becomes ubiquitous. In response to the advocacy in the “2021 Coordinated Plan on Artificial Intelligence”, it is crucial to understand how to leverage AI customer service chatbots for societal welfare. Across two scenario studies and one lab experiment, this research investigates the impact of AI chatbots’ communication styles on consumers’ subsequent prosocial intentions irrelevant to the AI-human interaction contents. The combined evidence suggests that consumers exhibit higher prosocial intentions after interacting with social-oriented (vs. task-oriented) AI chatbots. The findings reveal the chain-mediating roles of social presence and empathy. Moreover, the current research investigates the boundary effect of consumers’ goal focus (process focus vs. outcome focus), and shows that AI chatbots’ communication styles have stronger impact on prosocial intentions for customers with outcome focus. These results revealed the important externality of the AI application in marketplace and provide a novel perspective for companies to implement the corporate social responsibility (CSR) strategy.

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AI customer service chatbots / communication style / social presence / prosocial intention

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Yuanyuan Zhou, Jun Wang, Yixin Ding, Xinyu Meng, Ang Gao. Beyond Consumption-Relevant Outcomes: The Role of AI Customer Service Chatbots’ Communication Styles in Promoting Societal Welfare. Journal of Systems Science and Systems Engineering, 2025, 34(4): 448-470 DOI:10.1007/s11518-025-5674-8

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